Noometry

Model comparison

Claude Opus 4.5 vs Llama-3.3-70B-Instruct

Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 65× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.

Last verified . 28 shared benchmarks.

Claude Opus 4.5 Anthropic

50.5

Rank #47 Confirmed

Llama-3.3-70B-Instruct Meta

30.6

Rank #291 Confirmed

Summary

  • They share 28 benchmarks with published results for both. Claude Opus 4.5 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Claude Opus 4.5 leads 42.6 to 14.1.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.1% for Claude Opus 4.5 and 5.1% for Llama-3.3-70B-Instruct.
  • Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
  • Claude Opus 4.5 accepts more context: 200K tokens versus 128K.
  • Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

Side by side

Claude Opus 4.5Llama-3.3-70B-Instruct
ProviderAnthropicMeta
Noometry Index50.530.6
Released2025-11-012024-12-06
WeightsProprietaryOpen
Context window200K128K
Max output64K4K
Input $ / M tokens$5$0.10
Output $ / M tokens$25$0.32
Results tracked6943

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Category by category

Coding Claude Opus 4.5 leads

Claude Opus 4.5: 54.8 (#27), Llama-3.3-70B-Instruct: 31.0 (#290)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
WeirdML63.7%14.4%
LMArena Coding15041268
SWE-bench Verified76.7%—
SWE-bench Verified (bash only)76.8%—
LMArena WebDev1494—
SWE-bench Multilingual70.7%—
SciCode—26%
GSO26.5%—
BigCodeBench Instruct—46.9%
LiveBench Coding—36.6%
BigCodeBench Complete—57.5%
ALE-Bench1,025—
AlgoTune1.77—

Agentic & Tool Use Claude Opus 4.5 leads

Claude Opus 4.5: 47.3 (#12), Llama-3.3-70B-Instruct: 25.8 (#105)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
Berkeley Function Calling Leaderboard77.5%31.9%
BALROG43.5%23%
Terminal-Bench63.1%—
GDPval45.5%—
Remote Labor Index3.8%—
τ²-bench Airline84%—
τ²-bench Banking24.7%—
τ²-bench Retail79.6%—
τ²-bench Telecom92.3%—
Cybench82%—
DeepResearch Bench54.8%—
OSWorld66.3%—
LMArena Search1180—
METR Time Horizons75%—
Vending-Bench 24,967—

Reasoning Claude Opus 4.5 leads

Claude Opus 4.5: 42.6 (#51), Llama-3.3-70B-Instruct: 14.1 (#327)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
SimpleBench62%19.9%
LMArena Hard Prompts14761257
DTBench89.9%59.5%
LMCA44.5%17.5%
Epoch Capabilities Index150.09127.33
ForecastBench60.758.6
ARC-AGI-237.6%—
Kagi LLM Benchmark80.2%—
NYT Connections (extended)52.5%—
ARC-AGI-180%—
CritPt—0%
Chess Puzzles12%—
EnigmaEval11.9%—
EBR-Bench14.3%—
LiveBench Reasoning—50.8%
Mystery Game Puzzles22%—
LiveBench Data Analysis—49.5%
LiveBench—50.2%

Math Claude Opus 4.5 leads

Claude Opus 4.5: 38.6 (#132), Llama-3.3-70B-Instruct: 15.3 (#298)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
OTIS Mock AIME 2024-202586.1%5.1%
LMArena Math14631267
FrontierMath (Tiers 1-3)34.4%—
FrontierMath Tier 44.9%—
ProofBench36%—
LiveBench Math—42.2%
MATH Level 5—41.6%
FrontierMath (Feb 2025 set)20.7%—
FrontierMath Tier 4 (v1)4.2%—

Knowledge Claude Opus 4.5 leads

Claude Opus 4.5: 56.5 (#44), Llama-3.3-70B-Instruct: 30.6 (#226)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
GPQA Diamond86%47.4%
Vectara Hallucination Rate10.9%4.1%
LMArena Expert14871225
Humanity's Last Exam25.2%—
SimpleQA Verified45.7%—
Confabulations—22.8%
MMLU—86.3%

Multimodal Not comparable

Claude Opus 4.5: 31.4 (#107), Llama-3.3-70B-Instruct: —

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
GeoBench75%—
VPCT40%—
Furniture Assembly28.3%—
LMArena Document1462—

Multilingual Claude Opus 4.5 leads

Claude Opus 4.5: 54.3 (#47), Llama-3.3-70B-Instruct: 39.9 (#220)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
LMArena Non-English14381236
LMArena Chinese14701217
LMArena French14711281
LMArena German14491251
LMArena Japanese14161150
LMArena Korean14241143
LMArena Russian14471252
LMArena Spanish14581270

Instruction Following Claude Opus 4.5 leads

Claude Opus 4.5: 77.5 (#19), Llama-3.3-70B-Instruct: 71.1 (#157)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
LMArena Instruction Following14781242
LiveBench Instruction Following—82.7%

Long Context Claude Opus 4.5 leads

Claude Opus 4.5: 46.5 (#22), Llama-3.3-70B-Instruct: 26.4 (#295)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
LMArena Longer Query14801256
Fiction.LiveBench—33.3%
CL-bench21.1%—

Writing & Preference Claude Opus 4.5 leads

Claude Opus 4.5: 68.1 (#28), Llama-3.3-70B-Instruct: 47.6 (#207)

BenchmarkClaude Opus 4.5Llama-3.3-70B-Instruct
LMArena Text14511274
LMArena Creative Writing14451250
LMArena Multi-Turn14661280
EQ-Bench Creative Writing1687—
LiveBench Language—39.2%

Frequently asked questions

Is Claude Opus 4.5 better than Llama-3.3-70B-Instruct?

Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 65× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.

Which is cheaper, Claude Opus 4.5 or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Claude Opus 4.5 lists at $5 and $25.

Is Claude Opus 4.5 or Llama-3.3-70B-Instruct better for coding?

Claude Opus 4.5 scores higher on coding benchmarks: 54.8 versus 31.0 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.5 does, with 200K tokens against 128K.

How many benchmarks do Claude Opus 4.5 and Llama-3.3-70B-Instruct share?

28 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and Llama-3.3-70B-Instruct has 43.

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